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dc.contributor.authorEbadi, SEen_US
dc.contributor.authorIzquierdo, Een_US
dc.date.accessioned2017-09-13T12:21:20Z
dc.date.available2016-07-11en_US
dc.date.issued2016en_US
dc.date.submitted2017-09-13T13:10:09.746Z
dc.identifier.isbn978-3-319-46447-3en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/25666
dc.format.extent314 - 329en_US
dc.rightsThis is a pre-copyedited, author-produced version of an article accepted for publication in Lecture Notes in Computer Science following peer review. https://link.springer.com/chapter/10.1007%2F978-3-319-46448-0_19
dc.titleForeground Segmentation via Dynamic Tree-Structured Sparse RPCAen_US
dc.typeConference Proceeding
dc.rights.holder© Springer International Publishing AG 2016
dc.identifier.doi10.1007/978-3-319-46448-0_19en_US
pubs.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000389382700019&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.notesNot knownen_US
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Electronic Engineering - Research Students
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff
pubs.organisational-group/Queen Mary University of London/Faculty Reporting - Research Students
pubs.organisational-group/Queen Mary University of London/Faculty Reporting - Research Students/Faculty of Science & Engineering PGRs
pubs.organisational-group/Queen Mary University of London/REF
pubs.organisational-group/Queen Mary University of London/REF/REF - S&E - EECS UoA12
pubs.organisational-group/Queen Mary University of London/REF/REF - UoA 11
pubs.organisational-group/Queen Mary University of London/REF/REF - UoA 12
pubs.publication-statusPublisheden_US
pubs.volume9905en_US
qmul.funderLarge Scale Information Exploitation of Forensic Data (LASIE)::European Commissionen_US


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